Stanley: The Robot that Won the DARPA Grand Challenge
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara V. Nefian,Pamela Mahoney +29 more
TLDR
The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.Abstract:
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.read more
Citations
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Journal ArticleDOI
Automotive Technology and Human Factors Research: Past, Present, and Future
TL;DR: A review of automotive technology development and human factors research, largely by decade, since the inception of the automobile can be found in this article, where the human factors aspects were classified into primary driving task aspects (controls, displays, and visibility), driver workspace (seating and packaging, vibration, comfort, and climate), driver condition (fatigue and impairment), crash injury, advanced driver-assistance systems, external communication access, and driving behavior).
Proceedings ArticleDOI
Strategic decision making for automated driving on two-lane, one way roads using model predictive control
Julia Nilsson,Jonas Sjöberg +1 more
TL;DR: An algorithm for strategic decision making regarding when lane change and overtake manoeuvres are desirable and feasible by considering the task of driving on two-lane, one-way roads, as the selection of desired lane and velocity profile is presented.
The MIT - Cornell Collision and Why It Happened.
Luke Fletcher,Seth Teller,Edwin Olson,David Moore,Yoshiaki Kuwata,Jonathan P. How,John J. Leonard,Isaac Miller,Mark Campbell,Daniel P. Huttenlocher,Aaron Nathan,Frank-Robert Kline +11 more
TL;DR: The root causes of the collision are examined, which are identified in both teams’ system designs and include difficulties in sensor data association leading to phantom obstacles and an inability to detect slow moving vehicles.
Posted Content
Multi-timescale Nexting in a Reinforcement Learning Robot
TL;DR: This paper presents results with a robot that learns to next in real time, making thousands of predictions about sensory input signals at timescales from 0.1 to 8 seconds, and extends nexting beyond simple timescale by letting the discount rate be a function of the state.
Posted Content
Flow: A Modular Learning Framework for Autonomy in Traffic
TL;DR: Important technical challenges arising from the partial adoption of autonomy, to involve both AVs and human-driven vehicles, are tackled: partial control, partial observation, complex multi-vehicle interactions, and the sheer variety of traffic settings represented by real-world networks.
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